    README for the Benchmarks simulations associated with the following
    review paper:

    Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM,
    Diesmann M, Morrison A, Goodman PH, Harris Jr. FC, Zirpe M,
    Natschlager T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A,
    Rochel O, Vieville T, Muller E, Davison AP, ElBoustani S and
    Destexhe A.  Simulation of networks of spiking neurons: A review
    of tools and strategies.  Journal of Computational Neuroscience,
    vol 23, p 349-398, 2007

    Abstract:

    We review different aspects of the simulation of spiking neural
    networks.  We start by reviewing the different types of simulation
    strategies and algorithms that are currently implemented.  We next
    review the precision of those simulation strategies, in particular
    in cases where plasticity depends on the exact timing of the
    spikes.  We overview different simulators and simulation
    environments presently available (restricted to those freely
    available, open source and documented).  For each simulation tool,
    its advantages and pitfalls are reviewed, with an aim to allow the
    reader to identify which simulator is appropriate for a given task.
    Finally, we provide a series of benchmark simulations of different
    types of networks of spiking neurons, including Hodgkin-Huxley
    type, integrate-and-fire models, interacting with current-based or
    conductance-based synapses, using clock-driven or event-driven
    integration strategies.  The same set of models are implemented on
    the different simulators, and the codes are made available.  The
    ultimate goal of this review is to provide a resource to facilitate
    identifying the appropriate integration strategy and simulation
    tool to use for a given modeling problem related to spiking neural
    networks.

    The codes included in this package refer to the benchmark
    simulations described above.  The main goal is to provide a series
    of benchmark simulations of networks of spiking neurons, and how
    these are implemented in the different simulators overviewed in the
    paper.  See details in the enclosed file Appendix2.pdf, which
    describes these different benchmarks.  Some of these benchmarks
    were based on the Vogels-Abbott model (Vogels TP and Abbott LF.
    Signal propagation and logic gating in networks of
    integrate-and-fire neurons. J. Neurosci. 25: 10786-10795, 2005).

    The submitted version of the paper is available at this URL:

    http://arxiv.org/abs/q-bio.NC/0611089

    These files updated October 15th, 2008 to include Brian and PyNN.

    20110805 Ted Carnevale corrected the synaptic time constants for the
    NEURON implemetation of the coba model (see NEURON/coba/cobacell.hoc).